Zobrazeno 1 - 10
of 1 929
pro vyhledávání: '"P Staib"'
Autor:
Wang, Jiyao, Dvornek, Nicha C., Duan, Peiyu, Staib, Lawrence H., Ventola, Pamela, Duncan, James S.
Task-based fMRI uses actions or stimuli to trigger task-specific brain responses and measures them using BOLD contrast. Despite the significant task-induced spatiotemporal brain activation fluctuations, most studies on task-based fMRI ignore the task
Externí odkaz:
http://arxiv.org/abs/2406.12065
Autor:
You, Chenyu, Min, Yifei, Dai, Weicheng, Sekhon, Jasjeet S., Staib, Lawrence, Duncan, James S.
Fine-tuning pre-trained vision-language models, like CLIP, has yielded success on diverse downstream tasks. However, several pain points persist for this paradigm: (i) directly tuning entire pre-trained models becomes both time-intensive and computat
Externí odkaz:
http://arxiv.org/abs/2403.07241
Autor:
Guo, Xueqi, Shi, Luyao, Chen, Xiongchao, Liu, Qiong, Zhou, Bo, Xie, Huidong, Liu, Yi-Hwa, Palyo, Richard, Miller, Edward J., Sinusas, Albert J., Staib, Lawrence H., Spottiswoode, Bruce, Liu, Chi, Dvornek, Nicha C.
Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82-Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases. However, the hi
Externí odkaz:
http://arxiv.org/abs/2402.09567
Autor:
Zhang, Xiaoran, Stendahl, John C., Staib, Lawrence, Sinusas, Albert J., Wong, Alex, Duncan, James S.
We propose an adaptive training scheme for unsupervised medical image registration. Existing methods rely on image reconstruction as the primary supervision signal. However, nuisance variables (e.g. noise and covisibility), violation of the Lambertia
Externí odkaz:
http://arxiv.org/abs/2312.00837
Autor:
Zhang, Xiaoran, Pak, Daniel H., Ahn, Shawn S., Li, Xiaoxiao, You, Chenyu, Staib, Lawrence H., Sinusas, Albert J., Wong, Alex, Duncan, James S.
Deep learning methods for unsupervised registration often rely on objectives that assume a uniform noise level across the spatial domain (e.g. mean-squared error loss), but noise distributions are often heteroscedastic and input-dependent in real-wor
Externí odkaz:
http://arxiv.org/abs/2312.00836
Insufficiency of training data is a persistent issue in medical image analysis, especially for task-based functional magnetic resonance images (fMRI) with spatio-temporal imaging data acquired using specific cognitive tasks. In this paper, we propose
Externí odkaz:
http://arxiv.org/abs/2308.15564
Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation. Towards this end, recent deep learning-based medical segmentation methods have shown great promise in be
Externí odkaz:
http://arxiv.org/abs/2304.03209
Autor:
You, Chenyu, Dai, Weicheng, Min, Yifei, Staib, Lawrence, Sekhon, Jasjeet S., Duncan, James S.
Medical data often exhibits long-tail distributions with heavy class imbalance, which naturally leads to difficulty in classifying the minority classes (i.e., boundary regions or rare objects). Recent work has significantly improved semi-supervised m
Externí odkaz:
http://arxiv.org/abs/2304.02689
Autor:
You, Chenyu, Dai, Weicheng, Min, Yifei, Liu, Fenglin, Clifton, David A., Zhou, S Kevin, Staib, Lawrence Hamilton, Duncan, James S
For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples. This is enabled by the observation that without access
Externí odkaz:
http://arxiv.org/abs/2302.01735
Autor:
You, Chenyu, Dai, Weicheng, Liu, Fenglin, Min, Yifei, Dvornek, Nicha C., Li, Xiaoxiao, Clifton, David A., Staib, Lawrence, Duncan, James S.
Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly focus on instance discrimination and invariant mapping. However, they fa
Externí odkaz:
http://arxiv.org/abs/2209.13476